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Perhaps the earliest portrayal of the cross by Christians occurred in the iconography of their papyrus manuscripts, specifically the Staurogram, or shape of the cross made by the overlapping of the Greek letters “Rho” and “Tau” (). 11 A more obvious depiction of the cross is seen in a third-century gem in the British Museum, which depicts a crucified Jesus with an inscription that ...
Nailing to a cross is “less severe” and “less humiliating” as the condemned dies within a day from loss of blood. Tying to a cross is the most severe form of punishment usually reserved for robbers. Insects invited by the stench from the body fluid crawl in and out of the condemned’s crevices.
这篇文章中，讨论的Cross Entropy损失函数常用于分类问题中，但是为什么它会在分类问题中这么有效呢？我们先从一个简单的分类例子来入手。 1. 图像分类任务. 我们希望根据图片动物的轮廓、颜色等特征，来预测动物的类别，有三种可预测类别：猫、狗、猪。
Gospel accounts of Jesus’s execution do not specify how exactly Jesus was secured to the cross. Yet in Christian tradition, Jesus had his palms and feet pierced with nails. Even though Roman execution methods did include crucifixion with nails, some scholars believe this method only developed after Jesus’s lifetime.
I get the output 'Cross val score: -0.4412345093041985'. What is going on here? As I understand it, R^2 should be literally (0.7)^2 for a linear regression like this, and if there's some noise introduced by the cross-val split you'd expect it to be +- a decimal place or two, not a negative score.
Then I came across the K-fold cross validation approach and what I don’t understand is how I can relate the Test subset from the above approach. Meaning, in 5-fold cross validation we split the data into 5 and in each iteration the non-validation subset is used as the train subset and the validation is used as test set.
But the cross had nothing to do with Jesus Christ. The New Catholic Encyclopedia explains: “The cross is found in both pre-Christian and non-Christian cultures.” Jesus did not die on a cross. The Greek words translated “cross” basically mean “an upright stake,” “a timber,” or “a tree.”
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$\begingroup$ @imavv With a test score considerably worse than your val scores I'd take a step back and revisit your whole pipeline, e.g. there could be a problem with your validation strategy (like overfitting to your cross-validation or the test data stemming from a very different distribution than your train data).
(二) 适用条件判断 1.适用条件. 首先对数据进行简单查看，比如是否含有异常值等，异常值的存在可能会影响分析结果，所以在分析前需要进行处理，除此之外，进行方差分析还需要满足每个总体服从正态分布，各个总体的方差相同（满足方差齐性），以及观测值是独立的(例子已满足)。